Road Condition Prediction Based on ARIMA Algorithm

Abstract

This study investigates a road condition prediction method based on autoregressive integral moving average model (ARIMA) and its application in road traffic management and planning. With the acceleration of urbanization and the increasing demand for transportation, effective road condition prediction is crucial for traffic management and planning. This paper first introduces the challenges faced in the field of road transportation. Then, the principles and applications of the ARIMA algorithm in road condition prediction are elaborated, focusing on its advantages in capturing trends and cyclical changes in road traffic data. Subsequently, this paper verifies the effectiveness and usefulness of the ARIMA algorithm in road condition prediction through empirical analysis and case studies. The results show that the ARIMA algorithm exhibits high accuracy and stability in short- and medium-term road condition prediction, providing a simple and effective prediction tool for traffic management authorities and planners. Finally, this paper provides an outlook on the future research direction, presenting research outlooks on model optimization and improvement, combining other methods, and real-time prediction and application, in order to further improve the accuracy and practicality of road condition prediction.